Given the task definition, example input & output, solve the new input case.
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No
Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

New input case for you: To answer RQ3, which is concerned with domain adaptation, we can see in the grayed out cells in tab:results, which correspond to the cross-domain adaption case where the BERT language model is trained on the target domain, that domain adaptation works well with $2.2\%$ absolute accuracy improvement on the laptops test set and even $3.6\%$ accuracy improvement on the restaurants test set compared to BERT-base. 
 Question: By how much does their model outperform the baseline in the cross-domain evaluation?
Output:
$2.2\%$ absolute accuracy improvement on the laptops test set $3.6\%$ accuracy improvement on the restaurants test set